Multi-Label Datasets with Missing Values

Consisting of six multi-label datasets from the UCI Machine Learning repository. Each dataset contains missing values which have been artificially added at the following rates: 5, 10, 15, 20, 25, and30%. The “amputation” was performed using the “Missing Completely at Random” mechanism. File names are represented as follows: amp_DB_MR.arff where: DB = original dataset; MR = missing rate. If you use any of the resources available here or for more information: Jacob Junior, A. F. L., do Carmo, F. A., de Santana, A. L., Santana, E. E. C., & Lobato, F. M. F. (2024). EvoImp: Multiple Imputation of Multi-label Classification data with a genetic algorithm. Plos one, 19(1), e0297147. https://doi.org/10.1371/journal.pone.0297147